Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation

Wang Changhan
Wang Changhan
Pino Juan
Pino Juan

INTERSPEECH, pp. 4731-4735, 2020.

Cited by: 0|Bibtex|Views25|DOI:https://doi.org/10.21437/Interspeech.2020-2955
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Other Links: arxiv.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Transfer learning from high-resource languages is known to be an efficient way to improve end-to-end automatic speech recognition (ASR) for low-resource languages. Pre-trained or jointly trained encoder-decoder models, however, do not share the language modeling (decoder) for the same language, which is likely to be inefficient for dist...More

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